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<<<<<<< Updated upstream ======= <<<<<<< HEAD

Test Test Test

library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)

======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes

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<<<<<<< Updated upstream ======= <<<<<<< HEAD <<<<<<< HEAD

library(readxl)
hardship_complete <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/data/Hardship_complete.xlsx")
hardship_complete

hardship_complete$mean_homicide=log(hardship_complete$mean_homicide)
hardship_complete$gdp=log(hardship_complete$gdp)
hardship_complete$Infant_mortality=log(hardship_complete$Infant_mortality)
hardship_complete$life_expect=log(hardship_complete$life_expect)
hardship_complete$gini_income=log(hardship_complete$gini_income)
hardship_complete$`primary_ female_ enrollment_ rate`=log(hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

# Reverse Codierung
hardship_complete$mean_homicide=scale(hardship_complete$mean_homicide)
hardship_complete$gdp=scale(-hardship_complete$gdp)
hardship_complete$Infant_mortality=scale(hardship_complete$Infant_mortality)
hardship_complete$life_expect=scale(-hardship_complete$life_expect)
hardship_complete$gini_income=scale(hardship_complete$gini_income)
hardship_complete$gini_income=scale(-hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

hardship_complete$hardship_index=(hardship_complete$mean_homicide+hardship_complete$gdp+hardship_complete$gini_income+hardship_complete$life_expect+hardship_complete$Infant_mortality+hardship_complete$`primary_ female_ enrollment_ rate`)/6

hardship_complete

=======

library(readxl)
hardship_complete <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/code/Hardship_complete_2024.xlsx")
hardship_complete

NA
NA

hardship_complete$homiciderate=log(hardship_complete$homiciderate)
hardship_complete$gdp=log(hardship_complete$gdp)
hardship_complete$infantmortality=log(hardship_complete$infantmortality)
hardship_complete$lifeexpectancy=log(hardship_complete$lifeexpectancy)

hardship_complete

# Reverse Codierung
hardship_complete$homiciderate=scale(hardship_complete$homiciderate)
hardship_complete$gdp=scale(-hardship_complete$gdp)
hardship_complete$infantmortality=scale(hardship_complete$infantmortality)
hardship_complete$lifeexpectancy=scale(-hardship_complete$lifeexpectancy)
hardship_complete$gini=scale(hardship_complete$gini)
hardship_complete$femalemale_primedu=scale(-hardship_complete$femalemale_primedu)
hardship_complete

hardship_complete$hardship_index=(hardship_complete$homiciderate+hardship_complete$gdp+hardship_complete$gini+hardship_complete$lifeexpectancy+hardship_complete$infantmortality+hardship_complete$femalemale_primedu)/6

hardship_complete

# Laura added this code
library(dplyr)

# Impute missing values in the hardship indicators using the median
hardship_complete <- hardship_complete %>%
  mutate(across(c("homiciderate", "gdp", "infantmortality", "lifeexpectancy", "gini", "femalemale_primedu"),
                ~ifelse(is.na(.), median(., na.rm = TRUE), .)))

hardship_complete

# Use the mutate function to change the country name
hardship_complete <- hardship_complete %>%
  mutate(country = ifelse(label == "Serbia and Montenegro", "Serbia", label))
hardship_complete

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> Stashed changes

Checking Effectiveness of Imputation Method

<<<<<<< Updated upstream ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df

<<<<<<< HEAD =======

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD

#rename the variables
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
<<<<<<< HEAD
WV5_data

colnames(WV5_data)



#select only the variables of interest
=======
WV5_data

colnames(WV5_data)

  [1] "wave"          "V1A"           "V1B"           "country_code"  "V2A"           "V3"            "V4"            "V4_CO"         "V5"           
 [10] "V5_CO"         "V6"            "V6_CO"         "V7"            "V7_CO"         "V8"            "V8_CO"         "V9"            "V9_CO"        
 [19] "V10"           "V11"           "V12"           "V13"           "V14"           "V15"           "V16"           "V17"           "V18"          
 [28] "V19"           "V20"           "V21"           "V22"           "V23"           "V24"           "V25"           "V26"           "V27"          
 [37] "V28"           "V29"           "V30"           "V31"           "V32"           "V33"           "V34"           "V35"           "V36"          
 [46] "V37"           "V38"           "V39"           "V40"           "V41"           "V42"           "V43"           "V43_01"        "V43_02"       
 [55] "V43_03"        "V43_04"        "V43_05"        "V43_06"        "V43_07"        "V43_08"        "V43_09"        "V43_10"        "V43_11"       
 [64] "V43_12"        "V43_13"        "V43_14"        "V43_15"        "V43_16"        "V43_17"        "V43_18"        "V43_19"        "V43_20"       
 [73] "V43_21"        "V43_22"        "V43_23"        "V43_24"        "V43_25"        "V43_26"        "V43_27"        "V43_28"        "V43_29"       
 [82] "V43_30"        "V44"           "V45"           "V46"           "V47"           "V48"           "V49"           "V50"           "V51"          
 [91] "V52"           "V53"           "V54"           "married"       "children"      "V57"           "V58"           "V59"           "V60"          
[100] "V61"           "V62"           "V63"           "V64"           "V65"           "V66"           "V67"           "V68"           "V69"          
[109] "V69_HK"        "V70"           "V70_HK"        "V71"           "V72"           "V73"           "V73_HK"        "V74"           "V74_HK"       
[118] "V75"           "V76"           "V77"           "V78"           "V79"           "V80"           "V81"           "V82"           "V83"          
[127] "V84"           "V85"           "risktaking"    "V87"           "V88"           "V89"           "V90"           "V91"           "V92"          
[136] "V93"           "V94"           "V95"           "V96"           "V97"           "V98"           "V99"           "V100"          "V101"         
[145] "V102"          "V103"          "V104"          "V105"          "V106"          "V107"          "V108"          "V109"          "V110"         
[154] "V111"          "V112"          "V113"          "V114"          "V115"          "V116"          "V117"          "V118"          "V119"         
[163] "V120"          "V121"          "V122"          "V123"          "V124"          "V125"          "V126"          "V127"          "V128"         
[172] "V129"          "V130"          "V130_CA_1"     "V130_IQ_1"     "V130_IQ_2"     "V130_IQ_3"     "V130_IQ_4"     "V130_NZ_1"     "V130_NZ_2"    
[181] "V131"          "V132"          "V133"          "V134"          "V135"          "V136"          "V137"          "V138"          "V139"         
[190] "V140"          "V141"          "V142"          "V143"          "V144"          "V145"          "V146_00"       "V146_01"       "V146_02"      
[199] "V146_03"       "V146_04"       "V146_05"       "V146_06"       "V146_07"       "V146_08"       "V146_09"       "V146_10"       "V146_11"      
[208] "V146_12"       "V146_13"       "V146_14"       "V146_15"       "V146_16"       "V146_17"       "V146_18"       "V146_19"       "V146_20"      
[217] "V146_21"       "V146_22"       "V147"          "V148"          "V149"          "V150"          "V151"          "V151_IQ_A"     "V151_IQ_B"    
[226] "V152"          "V153"          "V154"          "V155"          "V156"          "V157"          "V158"          "V159"          "V160"         
[235] "V161"          "V162"          "V163"          "V164"          "V165"          "V166"          "V167"          "V168"          "V169"         
[244] "V170"          "V171"          "V172"          "V173"          "V174"          "V175"          "V176"          "V177"          "V178"         
[253] "V179"          "V180"          "V181"          "V182"          "V183"          "V184"          "V185"          "V186"          "V187"         
[262] "V188"          "V189"          "V190"          "V191"          "V192"          "V193"          "V194"          "V195"          "V196"         
[271] "V197"          "V198"          "V199"          "V200"          "V201"          "V202"          "V203"          "V204"          "V205"         
[280] "V206"          "V207"          "V208"          "V209"          "V210"          "V211"          "V212"          "V213A"         "V213B"        
[289] "V213C"         "V213D"         "V213E"         "V213F"         "V213G"         "V213H"         "V213K"         "V213L"         "V213M"        
[298] "V213N"         "V214"          "V215"          "V216"          "V217"          "V218"          "V219"          "V220"          "V221"         
[307] "V222"          "V223"          "V224"          "V225"          "V226"          "V227"          "V228"          "V229"          "V230"         
[316] "V231"          "V232"          "V233"          "V233A"         "V234"          "gender"        "V236"          "age"           "education"    
[325] "V238CS"        "V239"          "V240"          "employed"      "V242"          "V242A_CO"      "V243"          "V244"          "V245"         
[334] "V246"          "V247"          "V248"          "V249"          "V250"          "V251"          "V252"          "V252B"         "V253"         
[343] "V253CS"        "V254"          "V255"          "V255CS"        "V256"          "V257"          "V257B"         "V257C"         "V258"         
[352] "V259"          "V259A"         "V260"          "V261"          "V262"          "V263"          "V264"          "V265"          "S024"         
[361] "S025"          "Y001"          "Y002"          "Y003"          "SACSECVAL"     "SECVALWGT"     "RESEMAVAL"     "WEIGHTB"       "I_AUTHORITY"  
[370] "I_NATIONALISM" "I_DEVOUT"      "DEFIANCE"      "WEIGHT1A"      "I_RELIGIMP"    "I_RELIGBEL"    "I_RELIGPRAC"   "DISBELIEF"     "WEIGHT2A"     
[379] "I_NORM1"       "I_NORM2"       "I_NORM3"       "RELATIVISM"    "WEIGHT3A"      "I_TRUSTARMY"   "I_TRUSTPOLICE" "I_TRUSTCOURTS" "SCEPTICISM"   
[388] "WEIGHT4A"      "I_INDEP"       "I_IMAGIN"      "I_NONOBED"     "AUTONOMY"      "WEIGHT1B"      "I_WOMJOB"      "I_WOMPOL"      "I_WOMEDU"     
[397] "EQUALITY"      "WEIGHT2B"      "I_HOMOLIB"     "I_ABORTLIB"    "I_DIVORLIB"    "CHOICE"        "WEIGHT3B"      "I_VOICE1"      "I_VOICE2"     
[406] "I_VOI2_00"     "VOICE"         "WEIGHT4B"      "S001"          "S007"          "S018"          "S019"          "S021"          "COW"          

#select only the variables of interest
>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, married, employed, education)
WV5_data

<<<<<<< HEAD

# Read countrynames data from the CSV file (to decode the dataset 5)
countrynames <- read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

# Display the updated WV5_data
print(WV5_data)

=======

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429

#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)

<<<<<<< HEAD =======

#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD

WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)


#select only the variables of interest

WV6_data <- WV6_data %>%
  dplyr::select(wave, gender, age, country_code,risktaking, children, married, employed, education)
WV6_data

countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)
WV6_data

<<<<<<< HEAD

WV5_data
WV6_data
WVS_data = rbind(WV5_data, WV6_data)
WVS_data

unique(WVS_data$age)
range(WVS_data$age)

=======

WVS_data = rbind(WV5_data, WV6_data)
WVS_data

country_counts <- WVS_data %>%
  count(country)

# Print the result
print(country_counts)

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429

WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave5 = subset(WV5_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave6 = subset(WV6_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
WVS_data <- na.omit(WVS_data)
data_Wave5 <- na.omit(data_Wave5)
data_Wave6 <- na.omit(data_Wave6)









# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))

<<<<<<< HEAD

# Transfrom risk item such that high values represent more risk taking
WVS_data$risktaking = 6 - WVS_data$risktaking + 1

  
# Transform risk variable into T-score (mean = 50, sd = 10)
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

WVS_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data

=======

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> Stashed changes <<<<<<< Updated upstream ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD

unique(WVS_data$country)

<<<<<<< HEAD

WVS_mixed_model <- left_join(WVS_data, hardship, by = "country")
WVS_mixed_model
head(WVS_mixed_model)

library(lmerTest)

# intercept only model
model0 = lmer(Z_score_risktaking ~ 1 + (1|country),data = WVS_mixed_model)
summary_model0=summary(model0)
summary_model0

=======

 [1] "Andorra"             "Argentina"           "Australia"           "Brazil"              "Bulgaria"           
 [6] "Burkina Faso"        "Canada"              "Cyprus (G)"          "Chile"               "China"              
[11] "Egypt"               "Ethiopia"            "Finland"             "France"              "Georgia"            
[16] "Germany"             "Ghana"               "Hungary"             "India"               "Indonesia"          
[21] "Iran"                "Japan"               "Malaysia"            "Mali"                "Mexico"             
[26] "Moldova"             "Morocco"             "Netherlands"         "Norway"              "Peru"               
[31] "Poland"              "Romania"             "Russia"              "Rwanda"              "Slovenia"           
[36] "South Africa"        "South Korea"         "Spain"               "Sweden"              "Switzerland"        
[41] "Taiwan"              "Thailand"            "Trinidad and Tobago" "Turkey"              "Ukraine"            
[46] "United Kingdom"      "Uruguay"             "Viet Nam"            "Zambia"              "Algeria"            
[51] "Armenia"             "Azerbaijan"          "Belarus"             "Colombia"            "Ecuador"            
[56] "Estonia"             "Haiti"               "Iraq"                "Jordan"              "Kazakhstan"         
[61] "Kuwait"              "Kyrgyzstan"          "Lebanon"             "Libya"               "New Zealand"        
[66] "Nigeria"             "Pakistan"            "Palestine"           "Philippines"         "Qatar"              
[71] "Singapore"           "Tunisia"             "United States"       "Uzbekistan"          "Yemen"              
[76] "Zimbabwe"           

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429

# age, sex 
model1 = lmer(T_score_risktaking ~ 1 +scale(age)+factor(gender) + (1+scale(age)+factor(gender)|country),data = WVS_mixed_model, control = lmerControl(optimizer = "bobyqa"))
summary_model1=summary(model1)
summary_model1

<<<<<<< HEAD =======

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) +      factor(gender) | country)
   Data: WVS_mixed_model
Control: lmerControl(optimizer = "bobyqa")

REML criterion at convergence: 1081364

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4737 -0.7821 -0.0770  0.7536  3.2222 

Random effects:
 Groups   Name            Variance Std.Dev. Corr     
 country  (Intercept)      6.6199  2.5729            
          scale(age)       0.7621  0.8730   0.30     
          factor(gender)1  0.8747  0.9353   0.13 0.27
 Residual                 84.6046  9.1981            
Number of obs: 148527, groups:  country, 76

Fixed effects:
                Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)      51.4031     0.2977 75.2144  172.66   <2e-16 ***
scale(age)       -2.0093     0.1042 73.1211  -19.28   <2e-16 ***
factor(gender)1  -2.3174     0.1194 72.3693  -19.41   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g)
scale(age)  0.291        
fctr(gndr)1 0.072  0.236 

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429

#model 2
library(lme4)

library(lme4)
library(lmerTest)

# Define the lmer model and assign it to 'model_2'
model_2 <- lmer(T_score_risktaking ~ 1 + scale(age) + factor(gender) + factor(children) + 
                 factor(married) + factor(education) + factor(employed) +
                 (1 + scale(age) + factor(gender) + factor(children) + factor(employed) +
                  factor(married) | country),
                data = WVS_mixed_model, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))


# Display the summary of the model
summary_model_2 = summary(model_2)
summary_model_2

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(age) + factor(gender) + factor(children) +  
    factor(married) + factor(education) + factor(employed) +  
    (1 + scale(age) + factor(gender) + factor(children) + factor(employed) +          factor(married) | country)
   Data: WVS_mixed_model
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: 1079996

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5457 -0.7815 -0.0813  0.7425  3.1678 

Random effects:
 Groups   Name              Variance Std.Dev. Corr                         
 country  (Intercept)        5.4671  2.3382                                
          scale(age)         0.5367  0.7326    0.24                        
          factor(gender)1    0.9298  0.9642    0.03  0.24                  
          factor(children)1  0.7895  0.8885    0.07  0.19  0.11            
          factor(employed)1  0.3133  0.5597    0.04  0.03  0.02 -0.27      
          factor(married)1   0.4120  0.6419    0.22  0.43  0.57  0.20 -0.13
 Residual                   83.7217  9.1500                                
Number of obs: 148527, groups:  country, 76

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         5.204e+01  2.809e-01  8.000e+01 185.238  < 2e-16 ***
scale(age)         -1.478e+00  9.081e-02  7.228e+01 -16.275  < 2e-16 ***
factor(gender)1    -2.171e+00  1.234e-01  7.259e+01 -17.597  < 2e-16 ***
factor(children)1  -1.274e+00  1.284e-01  7.199e+01  -9.920 4.14e-15 ***
factor(married)1   -8.367e-01  9.872e-02  6.267e+01  -8.475 5.49e-12 ***
factor(education)1  7.809e-01  6.489e-02  1.407e+05  12.033  < 2e-16 ***
factor(employed)1   1.056e-01  8.590e-02  7.071e+01   1.229    0.223    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) fctr(g)1 fctr(c)1 fctr(mr)1 fctr(d)1
scale(age)   0.224                                            
fctr(gndr)1 -0.022  0.212                                     
fctr(chld)1 -0.016  0.047  0.032                              
fctr(mrrd)1  0.134  0.297  0.409   -0.083                     
fctr(dctn)1 -0.172  0.081  0.013    0.026   -0.013            
fctr(mply)1 -0.029  0.061  0.083   -0.203   -0.099    -0.063  

WVS_mixed_model

NA
NA

# age, sex 
model1 = lmer(T_score_risktaking ~ 1 +scale(age)+factor(gender) + (1+scale(age)+factor(gender)|country),data = WVS_mixed_model, control = lmerControl(optimizer = "bobyqa"))
summary_model1=summary(model1)
summary_model1

======= >>>>>>> Stashed changes <<<<<<< Updated upstream ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD <<<<<<< HEAD

model_3 <- lmer(T_score_risktaking ~ 1 + scale(z_score_age) * hardship_index + 
                    factor(gender) * hardship_index + factor(married) + factor(children) + 
                    factor(education) + factor(employed) + 
                    (1 + scale(z_score_age) + factor(married) + factor(children) + 
                     factor(education) + factor(employed) | country),
                data = WVS_mixed_model)

summary(model_3)

=======

model_3 <- lmer(T_score_risktaking ~ 1 + scale(age) * hardship_index + 
                    factor(gender) * hardship_index + factor(married) + factor(children) + 
                    factor(education) + factor(employed) + 
                    (1 + scale(age) + factor(married) + factor(children) + 
                     factor(education) + factor(employed) | country),
                data = WVS_mixed_model,control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))

summary_model_3 = summary(model_3) 

summary_model_3

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(age) * hardship_index + factor(gender) *  
    hardship_index + factor(married) + factor(children) + factor(education) +  
    factor(employed) + (1 + scale(age) + factor(married) + factor(children) +      factor(education) + factor(employed) | country)
   Data: WVS_mixed_model
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

REML criterion at convergence: 1080093

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.57571 -0.78110 -0.08036  0.74750  3.13409 

Random effects:
 Groups   Name               Variance Std.Dev. Corr                         
 country  (Intercept)         5.5510  2.3561                                
          scale(age)          0.3952  0.6286    0.17                        
          factor(married)1    0.4028  0.6347    0.13  0.13                  
          factor(children)1   0.8063  0.8980    0.09  0.06  0.25            
          factor(education)1  0.6332  0.7957   -0.20  0.03  0.07 -0.13      
          factor(employed)1   0.2881  0.5368    0.00  0.08 -0.29 -0.25  0.16
 Residual                    83.8078  9.1547                                
Number of obs: 148527, groups:  country, 76

Fixed effects:
                                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                     5.204e+01  2.862e-01  7.433e+01 181.838  < 2e-16 ***
scale(age)                     -1.442e+00  8.035e-02  7.109e+01 -17.949  < 2e-16 ***
hardship_index                  4.588e-01  3.840e-01  7.514e+01   1.195    0.236    
factor(gender)1                -2.081e+00  5.032e-02  1.285e+05 -41.359  < 2e-16 ***
factor(married)1               -8.145e-01  9.857e-02  6.114e+01  -8.263 1.53e-11 ***
factor(children)1              -1.302e+00  1.292e-01  7.512e+01 -10.070 1.39e-15 ***
factor(education)1              7.768e-01  1.205e-01  5.044e+01   6.447 4.24e-08 ***
factor(employed)1               1.031e-01  8.357e-02  6.975e+01   1.233    0.222    
scale(age):hardship_index       4.751e-01  1.122e-01  7.216e+01   4.233 6.68e-05 ***
hardship_index:factor(gender)1  5.799e-01  6.902e-02  6.340e+04   8.403  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) hrdsh_ fctr(g)1 fctr(mr)1 fctr(c)1 fctr(d)1 fctr(mp)1 sc():_
scale(age)   0.158                                                                    
hardshp_ndx  0.067  0.034                                                             
fctr(gndr)1 -0.098  0.042  0.002                                                      
fctr(mrrd)1  0.066  0.078 -0.005  0.044                                               
fctr(chld)1  0.005 -0.062 -0.003 -0.087   -0.054                                      
fctr(dctn)1 -0.285  0.080  0.014  0.016    0.030    -0.069                            
fctr(mply)1 -0.056  0.095  0.006  0.145   -0.190    -0.186    0.046                   
scl(g):hrd_  0.032  0.091  0.191  0.002   -0.005     0.005   -0.002   -0.035          
hrdshp_:()1 -0.002  0.011 -0.092  0.057   -0.012    -0.006    0.006    0.030     0.023

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD <<<<<<< HEAD

anova(model0,model1)
anova(model1,model_2)
anova(model_2,model_3) 

=======

======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes <<<<<<< Updated upstream ======= <<<<<<< HEAD

random_effects <- ranef(model_2)
random_effects

$country
                    (Intercept)   scale(age) factor(gender)1 factor(children)1 factor(employed)1 factor(married)1
Algeria              1.93351855 -0.854619155      0.87619718      -2.437881334        0.17698875     -0.377226443
Andorra              1.91309921  0.100909057     -0.66981587      -0.632350380        0.20524734     -0.686912835
Argentina           -1.57325789  0.057299092     -0.25780065      -1.067941885       -0.04621841     -0.553872390
Armenia              1.08284389 -0.454274344     -0.89771357      -0.834967062        0.17426467     -0.250770715
Australia           -0.14386428 -0.471375287     -1.06538548      -0.103260090        0.34608388     -0.365152174
Azerbaijan           0.45573171  0.004004243     -2.44218293       0.082693851       -0.21915868     -0.476480146
Belarus             -0.11536490 -1.000016058     -1.08120720      -0.467055853       -0.59713729     -0.046875401
Brazil              -2.84287325  0.262612027     -0.36729059      -0.148830175       -0.34944728     -0.655399532
Bulgaria            -0.77688584 -0.074197571      0.35410308       0.108171529        0.66545761     -0.168532781
Burkina Faso         1.61422833  1.156372503     -0.15049497       0.527033659       -0.25535458      0.423175478
Canada               0.39907784 -0.397232782     -1.02665629      -0.204786757        0.83298979     -0.733722779
Chile                1.03350248 -0.644167015      0.06242265      -1.191152814        0.30273676      0.182724595
China               -2.09375223 -0.445731013      0.50663419      -0.603547862       -0.41625835     -0.223192477
Colombia             0.22689773  0.422663335     -0.91548157      -0.850065668        0.05700229     -0.046926087
Cyprus (G)           2.25496876 -0.776460854     -0.79745197       0.050374871        0.20526698     -0.994122261
Ecuador              2.25294069 -0.040563584      0.75768462       1.082273600       -0.18216772      0.606966414
Egypt               -3.93763995  0.877857068      1.47333952      -0.977664255        0.69617277      0.506927589
Estonia             -1.06399862 -0.805260418     -0.20652339      -0.596528530        0.04115956     -0.263441563
Ethiopia             0.32293567  0.946455342      1.98005066      -0.482906872        1.19465275      0.520837889
Finland             -2.30711075 -0.193362752      0.06835226       0.109995921       -0.09624712     -0.446129133
France              -0.03262671 -0.315869185     -0.97254579      -0.756893462       -0.18841330     -0.721010031
Georgia             -0.34804857  0.313082762     -1.59932353       0.785006390        0.24885966     -0.322662614
Germany             -2.81688356 -0.866667483     -0.56600732      -0.067064088        0.10227997     -0.540483663
Ghana                4.20166977  0.817382387      1.03493510       1.320604468        0.06952553      0.935149120
Haiti               -6.53977492 -1.391127796      1.91289649       1.468940014       -0.89280260      0.533482158
Hungary             -1.99653091 -0.996752173     -0.17049773      -0.160714296        0.07124129     -0.186539589
India                1.43482568  0.617405603      0.45019893       1.836342889       -1.09159157      0.408036403
Indonesia            2.91129285  0.717729512     -0.37251725       0.036310599        0.61487372      0.351255947
Iran                 0.25704414 -0.273465758      0.84529203      -0.730085449        0.58356967     -0.185573571
Iraq                -1.06492016  0.012316662      0.43906584       0.138047194       -0.32549734      0.649008088
Japan               -6.33127212  0.980885912     -0.11206958       0.564989629       -0.44804909     -0.169376931
Jordan               1.80284181 -0.238660302     -0.04733756      -0.275114119        0.13106070      0.079727138
Kazakhstan          -2.61497176  0.099878889      0.38099799      -0.441649153        0.38909549      0.080016173
Kuwait               2.09145419 -1.486900320      0.10785659       0.216244412       -0.71982856     -0.372559647
Kyrgyzstan           0.61067751  1.072544199      1.07198178       0.701056752       -0.33081703      0.900953951
Lebanon              2.84470796 -0.023345343      0.08714121       0.523974067       -0.06162559      0.535210026
Libya                0.80327073 -0.475177836     -0.57060729       0.015373078        0.24676530     -0.259391475
Malaysia            -2.02257329  0.667498135      0.69083376       0.230243197       -0.12134511      0.648213460
Mali                 2.81563100  1.078784421      1.36765639       0.245910074       -0.30999444      1.287801120
Mexico              -0.40013922  0.561817933     -0.55226850      -1.131405064        0.01974498     -0.487453888
Moldova             -1.96423291 -0.329843137      1.07398956      -0.873396236        0.07687739      0.075323668
Morocco              0.68539027  0.249483178     -0.44534432      -0.309370915        0.01082943     -0.178509821
Netherlands         -2.06588418 -0.228085056     -0.89179246       0.412747451       -0.17127304     -0.633635212
New Zealand          0.60732681 -0.774713993      0.04302897      -0.175577939       -0.11512608     -0.279411963
Nigeria              5.59377567  1.275958637      1.62013094       0.389674844        0.37813269      1.361593143
Norway               0.99797819 -0.771979285      0.88446601      -0.045167601       -0.33921268     -0.013483356
Pakistan             4.22659980  1.325790041      0.51442825       0.928501617       -0.97792666      0.666654501
Palestine            0.03194219 -0.494835906     -0.14515969       0.292953341        0.50604479     -0.015691232
Peru                -1.61516779  0.633332407     -0.68009631      -0.717362556       -0.39962778     -0.181622730
Philippines          3.70377389  0.943369150      1.13088110       0.747509770       -0.08964864      0.356984425
Poland               2.19905667  0.037084593     -0.45404816      -0.900087529        0.25558657      0.232224025
Qatar                0.86028228  0.502376889     -0.94153784       0.200394431        0.45200189     -0.086160547
Romania             -2.52089552 -0.155453441     -0.24318156      -0.213424238        0.59038608     -0.046222935
Russia              -0.03564662  0.201433455     -0.04277014       0.338525048        0.55241645     -0.367819701
Rwanda               0.83617515  1.171444046      1.19476510       1.697279263       -0.29018529      0.335814980
Singapore            2.17597132  0.591444549      0.38684581      -0.017294940       -0.08165496      0.363947899
Slovenia             0.21102243 -1.245719908     -0.62662173      -0.617380784        0.28218849     -1.002740221
South Africa         2.37631040  0.311956458      1.26337006       1.080146122        0.68094981      0.751652547
South Korea          1.36893323 -0.405664050      0.80080531       0.341944092       -0.04931702      0.322300385
Spain                0.82809176 -0.466229484      0.62854866      -0.304041861        0.09422269     -0.358645793
Sweden               0.09920433 -0.328664254     -0.47169418      -0.328195601       -0.42679317     -0.438405460
Switzerland         -1.56290696  0.084346338     -1.48036381       0.605515233        0.14178415     -0.884764664
Taiwan              -4.35319381 -0.569177868     -0.09471962      -0.003120567       -0.38488863     -0.220827542
Thailand            -0.09358157  0.844688730      1.30128241       0.253396867       -0.53318528      1.057267847
Trinidad and Tobago  0.24160167 -0.343056024     -0.36770149       1.121472297        0.13881740      0.059010394
Tunisia             -0.42814434 -1.362242692     -0.53278180      -0.600878765       -0.28277064     -0.244015679
Turkey               0.94251912  0.120341608      0.37598170      -0.387346063        0.29373891      0.028586139
Ukraine             -1.58175919 -0.668879956     -0.36649799      -0.519892277        0.26972976     -0.001257245
United Kingdom       1.15668394 -0.030727372     -1.56593196      -0.489635482        0.10560093     -0.751106765
United States       -0.70200525 -0.752298720     -0.40433841       0.416924321        0.25359660     -0.253434481
Uruguay             -3.20629770 -0.108243534      0.34971574      -0.439094936       -0.31919572     -0.167863204
Uzbekistan           1.00109484  0.687012035     -0.28533330       0.716274322        0.20755393      0.141926144
Viet Nam            -3.44301278  0.598580704     -0.35309866       0.622440999       -0.56204886      0.334138408
Yemen               -3.91719513  0.062896576     -0.10339324      -0.646218561       -0.08885691     -0.219935374
Zambia               1.41695079  0.455513249      0.41766478       1.043367849       -0.57988565      1.261564524
Zimbabwe             1.68856739  0.396489975     -1.11595904       0.496697944       -0.32194635     -0.119112532

with conditional variances for “country” 

======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes

# Extract random effects for 'country'
# Assuming 'model' is your fitted lmer model
random_effects <- ranef(model_2)
random_effects

<<<<<<< Updated upstream ======= <<<<<<< HEAD

colnames(random_effects)

NULL

>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> Stashed changes

regression_results_WVS <- WVS_data %>%
  group_by(country) %>%
  do(model = lm(Z_score_risktaking ~ scale(age) + gender, data = .)) %>%
  summarize(
    country = first(country),
    intercept_regression = coef(summary(model))[1, 1],
    slope_age_regression = coef(summary(model))[2, 1],
    slope_gender_regression = coef(summary(model))[3, 1]
  )

regression_results_WVS

random_effects <- ranef(model_2)
random_effects

colnames(random_effects)

<<<<<<< Updated upstream ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes

coefsallmodels=rbind(summary_model1$coefficients,
summary_model_2$coefficients,
summary_model_3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")

# Read the CSV file into a data frame
gbd_mentalhealth <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/GBD_mentalhealth.xlsx")

gbd_mentalhealth

#select only the variables of interest
gbd_mentalhealth <- gbd_mentalhealth %>%
  dplyr::select(country, gender, age, cause, val, Measure)
gbd_mentalhealth


library(dplyr)

# Group data by country and age group, and calculate summary statistics
summary_by_country_age <- gbd_mentalhealth %>%
  group_by(country, age) %>%
  summarise(
    mean_DALYs = mean(val))  # Calculate mean of DALYs

library(dplyr)

# Assuming 'summary_by_country_age' contains your summarized dataset
mean_by_country <- summary_by_country_age %>%
  group_by(country) %>%
  summarise(mean_DALYs = mean(mean_DALYs))

# View the resulting mean by country
print(mean_by_country)

#log transform
mean_by_country$mean_DALYs=log(mean_by_country$mean_DALYs)
 
mean_by_country 

#Reverse codierung 
mean_by_country$mean_DALYs=scale(mean_by_country$mean_DALYs)

mean_by_country

#rename mean_DALYS
mental_health_index <- mean_by_country %>%
  rename('mental_health' = mean_DALYs)
mental_health_index

library(dplyr)

#######Anxiety disorders#########
#Filter data for a specific mental disorder (e.g., Anxiety Disorders)
anxiety_disorders <- gbd_mentalhealth %>%
  filter(cause == "Anxiety disorders")  # Change "Anxiety Disorders" to the desired disorder

# Calculate the mean of 'val' (Disability-Adjusted Life Years) across locations and ages
mean_DALYs <- mean(anxiety_disorders$val)

# Optionally, if you want to calculate mean by country:
anxiety_disorders <- anxiety_disorders %>%
  group_by(country) %>%
  summarise(mean_Anxiety_disorders = mean(val))

anxiety_disorders

#log transform
anxiety_disorders$mean_Anxiety_disorders=log(anxiety_disorders$mean_Anxiety_disorders)
 
anxiety_disorders

#Reverse codierung 
anxiety_disorders$mean_Anxiety_disorders=scale(anxiety_disorders$mean_Anxiety_disorders)
anxiety_disorders
mental_health_index

gbd_mentalhealth

# Assuming gbd_mentalhealth is your data frame containing the 'cause' column

# Get unique values of 'cause' column
unique_causes <- unique(gbd_mentalhealth$cause)

# Create a data frame with unique causes
cause_table <- data.frame(Cause = unique_causes)

# Print the cause table
print(cause_table)

#######Bulimia nervosa########
library(dplyr)

# Filter the data for 'Bulimia Nervosa'
bulimia_nervosa <- gbd_mentalhealth %>%
  filter(cause == "Bulimia nervosa")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(bulimia_nervosa$val)

# Optionally, calculate mean by country
bulimia_nervosa <- bulimia_nervosa %>%
  group_by(country) %>%
  summarise(mean_bulimia_nervosa = mean(val))

# Log transform the mean_bulimia_nervosa column
bulimia_nervosa$mean_bulimia_nervosa <- log(bulimia_nervosa$mean_bulimia_nervosa)

# Scale the mean_bulimia_nervosa column (if needed)
bulimia_nervosa$mean_bulimia_nervosa <- scale(bulimia_nervosa$mean_bulimia_nervosa)

# Print the resulting data frame
print(bulimia_nervosa)
print(anxiety_disorders)

gbd_mentalhealth

#####Attention-deficit/hyperactivity disorder
library(dplyr)


ADHD <- gbd_mentalhealth %>%
  filter(cause == "Attention-deficit/hyperactivity disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(ADHD$val)

# Optionally, calculate mean by country
ADHD <- ADHD %>%
  group_by(country) %>%
  summarise(mean_ADHD = mean(val))

# Log transform the mean_bulimia_nervosa column
ADHD$mean_ADHD <- log(ADHD$mean_ADHD)

# Scale the mean_bulimia_nervosa column (if needed)
ADHD$mean_ADHD <- scale(ADHD$mean_ADHD)

# Print the resulting data frame
print(ADHD)

#########Idiopathic development intellectual ability 
library(dplyr)


Idiopathic_developmental_intellectual_disability <- gbd_mentalhealth %>%
  filter(cause == "Idiopathic developmental intellectual disability")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(Idiopathic_developmental_intellectual_disability$val)

# Optionally, calculate mean by country
Idiopathic_developmental_intellectual_disability <- Idiopathic_developmental_intellectual_disability %>%
  group_by(country) %>%
  summarise(mean_Idiopathic_developmental_intellectual_disability = mean(val))

# Log transform the mean_bulimia_nervosa column
Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability <- log(Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability)

# Scale the mean_bulimia_nervosa column (if needed)
Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability <- scale(Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability)

# Print the resulting data frame
print(Idiopathic_developmental_intellectual_disability)

######Anorexia Nervosa

anorexia_nervosa <- gbd_mentalhealth %>%
  filter(cause == "Anorexia nervosa")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(anorexia_nervosa$val)

# Optionally, calculate mean by country
anorexia_nervosa <- anorexia_nervosa %>%
  group_by(country) %>%
  summarise(mean_anorexia_nervosa = mean(val))

# Log transform the mean_bulimia_nervosa column
anorexia_nervosa$mean_anorexia_nervosa <- log(anorexia_nervosa$mean_anorexia_nervosa)

# Scale the mean_bulimia_nervosa column (if needed)
anorexia_nervosa$mean_anorexia_nervosa <- scale(anorexia_nervosa$mean_anorexia_nervosa)

# Print the resulting data frame
print(anorexia_nervosa)

#####Depressive disorders#######

depressive_disorders <- gbd_mentalhealth %>%
  filter(cause == "Depressive disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(depressive_disorders$val)

# Optionally, calculate mean by country
depressive_disorders <- depressive_disorders %>%
  group_by(country) %>%
  summarise(mean_depressive_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
depressive_disorders$mean_depressive_disorders <- log(depressive_disorders$mean_depressive_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
depressive_disorders$mean_depressive_disorders <- scale(depressive_disorders$mean_depressive_disorders)

# Print the resulting data frame
print(depressive_disorders)

#######Autismus spectrum disorders######

autismus_spectrum_disorders <- gbd_mentalhealth %>%
  filter(cause == "Autism spectrum disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(autismus_spectrum_disorders$val)

# Optionally, calculate mean by country
autismus_spectrum_disorders <- autismus_spectrum_disorders %>%
  group_by(country) %>%
  summarise(mean_autismus_spectrum_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
autismus_spectrum_disorders$mean_autismus_spectrum_disorders <- log(autismus_spectrum_disorders$mean_autismus_spectrum_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
autismus_spectrum_disorders$mean_autismus_spectrum_disorders <- scale(autismus_spectrum_disorders$mean_autismus_spectrum_disorders)

# Print the resulting data frame
print(autismus_spectrum_disorders)

######Schizophrenia#######
schizophrenia <- gbd_mentalhealth %>%
  filter(cause == "Schizophrenia")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(schizophrenia$val)

# Optionally, calculate mean by country
schizophrenia <- schizophrenia %>%
  group_by(country) %>%
  summarise(mean_schizophrenia = mean(val))

# Log transform the mean_bulimia_nervosa column
schizophrenia$mean_schizophrenia <- log(schizophrenia$mean_schizophrenia)

# Scale the mean_bulimia_nervosa column (if needed)
schizophrenia$mean_schizophrenia <- scale(schizophrenia$mean_schizophrenia)

# Print the resulting data frame
print(schizophrenia)

#######Conduct disorders#########
conduct_disorders <- gbd_mentalhealth %>%
  filter(cause == "Conduct disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(conduct_disorders$val)

# Optionally, calculate mean by country
conduct_disorders <- conduct_disorders %>%
  group_by(country) %>%
  summarise(mean_conduct_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
conduct_disorders$mean_conduct_disorders <- log(conduct_disorders$mean_conduct_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
conduct_disorders$mean_conduct_disorders <- scale(conduct_disorders$mean_conduct_disorders)

# Print the resulting data frame
print(conduct_disorders)

########Eating disorders#########
eating_disorders <- gbd_mentalhealth %>%
  filter(cause == "Eating disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(eating_disorders$val)

# Optionally, calculate mean by country
eating_disorders <- eating_disorders %>%
  group_by(country) %>%
  summarise(mean_eating_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
eating_disorders$mean_eating_disorders <- log(eating_disorders$mean_eating_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
eating_disorders$mean_eating_disorders <- scale(eating_disorders$mean_eating_disorders)

# Print the resulting data frame
print(eating_disorders)

########Bipolar disorder#########
bipolar_disorder <- gbd_mentalhealth %>%
  filter(cause == "Bipolar disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(bipolar_disorder$val)

# Optionally, calculate mean by country
bipolar_disorder <- bipolar_disorder %>%
  group_by(country) %>%
  summarise(mean_bipolar_disorder = mean(val))

# Log transform the mean_bulimia_nervosa column
bipolar_disorder$mean_bipolar_disorder <- log(bipolar_disorder$mean_bipolar_disorder)

# Scale the mean_bulimia_nervosa column (if needed)
bipolar_disorder$mean_bipolar_disorder <- scale(bipolar_disorder$mean_bipolar_disorder)

# Print the resulting data frame
print(bipolar_disorder)

print(cause_table)

########Substance use disorders########

substance_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Substance use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(substance_use_disorders$val)

# Optionally, calculate mean by country
substance_use_disorders <- substance_use_disorders %>%
  group_by(country) %>%
  summarise(mean_substance_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
substance_use_disorders$mean_substance_use_disorders <- log(substance_use_disorders$mean_substance_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
substance_use_disorders$mean_substance_use_disorders <- scale(substance_use_disorders$mean_substance_use_disorders)

# Print the resulting data frame
print(substance_use_disorders)

####Drug use disorders

drug_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Drug use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(drug_use_disorders$val)

# Optionally, calculate mean by country
drug_use_disorders <- drug_use_disorders %>%
  group_by(country) %>%
  summarise(mean_drug_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
drug_use_disorders$mean_drug_use_disorders <- log(drug_use_disorders$mean_drug_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
drug_use_disorders$mean_drug_use_disorders <- scale(drug_use_disorders$mean_drug_use_disorders)

# Print the resulting data frame
print(drug_use_disorders)

##########Alcohol use disorders
alcohol_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Alcohol use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(alcohol_use_disorders$val)

# Optionally, calculate mean by country
alcohol_use_disorders <- alcohol_use_disorders %>%
  group_by(country) %>%
  summarise(mean_alcohol_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
alcohol_use_disorders$mean_alcohol_use_disorders <- log(alcohol_use_disorders$mean_alcohol_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
alcohol_use_disorders$mean_alcohol_use_disorders <- scale(alcohol_use_disorders$mean_alcohol_use_disorders)

# Print the resulting data frame
print(alcohol_use_disorders)

#######Major depressive disorders
major_depressive_disorder <- gbd_mentalhealth %>%
  filter(cause == "Major depressive disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(major_depressive_disorder$val)

# Optionally, calculate mean by country
major_depressive_disorder <- major_depressive_disorder %>%
  group_by(country) %>%
  summarise(mean_major_depressive_disorder = mean(val))

# Log transform the mean_bulimia_nervosa column
major_depressive_disorder$mean_major_depressive_disorder <- log(major_depressive_disorder$mean_major_depressive_disorder)

# Scale the mean_bulimia_nervosa column (if needed)
major_depressive_disorder$mean_major_depressive_disorder <- scale(major_depressive_disorder$mean_major_depressive_disorder)

# Print the resulting data frame
print(major_depressive_disorder)

#######Dysthymia########

dysthymia <- gbd_mentalhealth %>%
  filter(cause == "Dysthymia")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(dysthymia$val)

# Optionally, calculate mean by country
dysthymia <- dysthymia %>%
  group_by(country) %>%
  summarise(mean_dysthymia = mean(val))

# Log transform the mean_bulimia_nervosa column
dysthymia$mean_dysthymia <- log(dysthymia$mean_dysthymia)

# Scale the mean_bulimia_nervosa column (if needed)
dysthymia$mean_dysthymia <- scale(dysthymia$mean_dysthymia)

# Print the resulting data frame
print(dysthymia)

library(dplyr)

# Perform left joins for each data frame on 'country'
final_mental_health_index <- mental_health_index %>%
  left_join(bulimia_nervosa, by = "country") %>%
  left_join(ADHD, by = "country") %>%
  left_join(Idiopathic_developmental_intellectual_disability, by = "country") %>%
  left_join(anorexia_nervosa, by = "country") %>%
  left_join(depressive_disorders, by = "country") %>%
  left_join(autismus_spectrum_disorders, by = "country") %>%
  left_join(conduct_disorders, by = "country") %>%
  left_join(schizophrenia, by = "country") %>%
  left_join(eating_disorders, by = "country") %>%
  left_join(bipolar_disorder, by = "country") %>%
  left_join(drug_use_disorders, by = "country") %>%
  left_join(alcohol_use_disorders, by = "country") %>%
  left_join(substance_use_disorders, by = "country") %>%
  left_join(major_depressive_disorder, by = "country") %>%
  left_join(dysthymia, by = "country")

# Display the resulting data frame
print(final_mental_health_index)

# Show the first few rows of the resulting data frame
head(final_mental_health_index)

indicators <- left_join(final_mental_health_index, hardship, by = "country")
indicators
head(indicators)

new_data <- left_join (WVS_data, indicators, by = "country")
new_data


# Transfrom risk item such that high values represent more risk taking
new_data$risktaking = 6 - new_data$risktaking + 1

  
# Transform risk variable into T-score (mean = 50, sd = 10)
new_data$T_score_risktaking = 10*scale(new_data$risktaking, center=TRUE,scale=TRUE)+50

new_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
new_data$Z_score_risktaking = (new_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(new_data)

new_data <- new_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
new_data

<<<<<<< Updated upstream ======= <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes

library(lme4)

library(lme4)

model <- lmer(T_score_risktaking ~ scale(z_score_age) * mental_health +
               gender * mental_health +
               factor(married) + factor(children) +
               factor(education) + factor(employed) +
               (1 + scale(z_score_age) + factor(married) + factor(children) + 
                factor(education) + factor(employed) | country),
<<<<<<< Updated upstream
             data = new_data, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))
=======
<<<<<<< HEAD
<<<<<<< HEAD
             data = new_data)
=======
             data = new_data, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))
>>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429
=======
             data = new_data, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))
>>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648
>>>>>>> Stashed changes


summary(model)

# Assuming 'model' is a linear mixed-effects model (lmer), and you want to save coefficients to a CSV file

# Extract coefficients from the model summary
coefficients_df <- data.frame(summary(model)$coefficients)

# Write coefficients to a CSV file
write.csv(coefficients_df, "model_coefficients.csv", row.names = TRUE)

<<<<<<< Updated upstream ======= <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes

```

<<<<<<< Updated upstream ======= <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 4479a7ab7bae21012b85ea8788a321584f71e429 ======= >>>>>>> 102b2708b98624b69d3d62a25724b361d57d7648 >>>>>>> Stashed changes